An Optimization Design Method for Ship Power Control System

Abstract

This invention relates to the field of ship power control optimization, specifically to a method for optimizing the design of a ship power control system. The method includes physically deploying control nodes (including sensing nodes, decision nodes, and execution nodes) within the system, setting a corresponding self-organizing network topology for each operating condition, and using a condition identification algorithm built into the decision nodes for real-time analysis. Based on the judgment results, the decision nodes reconstruct instructions, disconnecting old connections and establishing new topology connections, and verifying topology connectivity and stability to complete the optimized design of the power control system. This invention overcomes the limitations of traditional fixed topologies by customizing exclusive self-organizing network topologies for different operating conditions such as acceleration, steering, and emergency response, achieving resource scheduling and avoiding energy waste from full-node operation. Furthermore, by allocating resources to core control needs, it improves the targeting and accuracy of power control under different operating conditions, solving the problems of poor operating condition adaptability and resource redundancy in traditional systems.

Description

Technical Field

[0001] This invention relates to the field of ship power control optimization, and more specifically to an optimization design method for a ship power control system. Background Technology

[0002] The ship's power control system consists of a power unit, a transmission system, and an auxiliary system, and uses sensors and controllers to achieve energy conversion and thrust regulation.

[0003] Existing marine power control systems mostly employ fixed topology designs, making it difficult to dynamically adjust node connections and control logic according to the ship's operating status. When switching between different operating conditions such as acceleration, turning, and emergency response, fixed topologies lead to the continuous operation of unnecessary nodes, resulting in energy redundancy. Simultaneously, traditional systems lack accurate operating condition identification and rapid adaptation mechanisms, easily exhibiting response lag during operating condition transitions, affecting the accuracy of control command execution. Furthermore, the complex marine navigation environment renders traditional node communication methods unstable in scenarios with strong electromagnetic interference, and redundant nodes and connection paths may interfere with core control processes during emergency situations, reducing system emergency reliability. Moreover, existing systems lack standardized design for node deployment and adaptation logic, making subsequent maintenance and expansion difficult and unable to adapt to the power system layout requirements of different types of ships. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing an optimized design method for a ship power control system.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: A method for optimizing the design of a ship power control system, the method comprising the following steps: S101. Physically deploy the control nodes, which include sensing nodes, decision nodes, and execution nodes, within the system, and complete the input of node IDs and basic information. S102. Based on the ship's operating scenarios, divide its working conditions into scenarios, and set the corresponding self-organizing network topology of control nodes based on each working condition to complete dynamic topology pre-configuration and rule setting. S103. Based on the sensing nodes and execution nodes, complete the collection of working condition data, status feedback data and operation instructions to form a three-dimensional monitoring dataset. Then, through the working condition identification algorithm built into the decision node, analyze it in real time to complete the working condition identification and threshold comparison. Based on the working condition identification and threshold comparison results, complete the topology adaptability judgment. S104. The decision node issues a reconstruction command based on the judgment result, disconnects from the old connection, establishes a new topology connection, and verifies the topology connectivity and stability, thus completing the optimization design of the power control system.

[0006] In a preferred embodiment, in S101, the physical deployment of the control node determines the installation location based on the ship's propulsion system layout, including: Sensing nodes are deployed in power equipment and steam pipelines, decision nodes are deployed in the control compartment, and execution nodes are coaxially installed with the system's actuators, such as forward actuators, steering actuators, and braking actuators. After deployment, each control node is assigned a unique binary ID, such as 01 for sensing nodes, 02 for decision nodes, and 03 for execution nodes.

[0007] In a preferred embodiment, the operating scenarios are divided according to the ship's operating conditions, including acceleration, turning, constant speed, braking, and emergency conditions, and a corresponding self-organizing network topology of control nodes is set for each operating scenario.

[0008] Accelerate the operation; retain all sensing nodes, focus on activating the turbine speed and steam injection quantity sensing nodes, call decision nodes, and establish connections between execution nodes in the order of forward actuator nodes, transmission regulation actuator nodes, and auxiliary stabilization actuator nodes, and establish a self-organizing network topology structure of sensing node cluster, decision nodes, and priority execution node sequence; Steering condition; increase the signal weight of the steering angle and hull tilt sensing nodes, call the decision node, and establish connections between the execution nodes in the order of forward actuator node, transmission adjustment actuator node, and auxiliary stabilization actuator node, and establish a self-organizing network topology of steering-related sensing nodes, decision nodes, and steering-specific execution node groups; Emergency conditions: Skip non-critical sensing nodes and retain only core sensing nodes such as turbine failure, braking pressure, and hull structural stress. Decision nodes are activated, and only emergency braking, emergency power cut-off, and emergency steering actuator nodes are retained as execution nodes. The topology is compressed into core sensing nodes, decision nodes, and emergency execution nodes to ensure the shortest control path. Uniform speed operation and braking operation; referring to the above logic, set low energy consumption topology rules to reduce the number of redundant sensing nodes in operation, and with kinetic energy recovery topology rules, prioritize the activation of execution nodes related to energy feedback closed-loop control. In a preferred embodiment, in S103, the decision node receives the collected data from all sensing nodes and the status feedback data from the execution nodes in real time through a preset communication protocol, and simultaneously receives the operation instructions from the ship's bridge to form a three-dimensional monitoring dataset. The working condition recognition algorithm built into the decision node extracts key feature parameters from the three-dimensional monitoring dataset and constructs a working condition recognition vector. The key feature parameters include operation command features, perception data features, and execution status features. Operation command features include extracting command type, core parameter amplitude, and parameter change rate. Perception data features include extracting the change trend of core perception parameters and parameter fluctuation amplitude. Execution status features include extracting the action response delay and action completion rate of the execution node. After acquiring key feature parameters, the working condition identification algorithm performs real-time analysis including: Preliminary operating condition classification is performed based on the characteristics of the operating commands. The preliminary operating condition classification corresponds to the divided operating condition scenarios, including acceleration, steering, constant speed, braking, and emergency scenarios. The classification process is as follows: the control node that collects the data, status feedback data, and operating commands corresponding to the operating condition scenarios based on the characteristics of the operating commands is used. For example, if the command type is detected as acceleration and the accelerator pedal opening is ≥50%, it is initially determined to be an acceleration condition. If the command type is detected as steering and the steering wheel rotation angle is ≥30, it is initially determined to be a steering condition. Based on historical navigation experience, the basic thresholds for switching operating conditions are preset. For example, the threshold for triggering acceleration is that the forward execution opening is ≥50% and lasts for 1 second; the threshold for triggering braking is that the braking execution opening is ≥40% or the braking pressure is ≤3MPa; and the threshold for triggering emergency is that the core sensing parameters exceed the safe range. The threshold parameters are stored in the decision node. The decision node compares the extracted feature parameters with the trigger threshold in real time, and determines the switching of working conditions according to the logic of meeting the threshold conditions and the duration reaching the standard.

[0009] In a preferred embodiment, it further includes: For the same type of working condition adjustment; if it does not exceed the basic threshold range of its working condition, it will not trigger topology reconfiguration, but will only update the control parameters. For example, in the acceleration working condition, if the opening of the forward actuator increases from 50% to 70%, but does not exceed the threshold range of the acceleration working condition, it is determined to be a parameter adjustment within the acceleration working condition, which will not trigger topology reconfiguration, but will only update the control parameters. When switching between different operating conditions, if the switch exceeds the basic threshold range of the operating condition but does not exceed the basic threshold range of the emergency operating condition, topology reconstruction will not be triggered and control parameters will be updated. Alternatively, if the switch exceeds the basic threshold range of the operating condition but is outside the basic threshold range of the emergency operating condition, topology adaptability judgment will be triggered and control parameters will be updated.

[0010] In a constant speed condition, if the opening of the forward actuator increases from 30% to 60% and lasts for 1.5s, it meets the trigger threshold for acceleration, and is determined to be a switch between constant speed and acceleration conditions. In a normal condition, if the turbine speed increases to 300r / min and lasts for 500ms, it meets the trigger threshold for emergency conditions, and is determined to be a switch between normal and emergency conditions, triggering topology reconfiguration.

[0011] In a preferred embodiment, after the decision node confirms the switching of operating conditions, it calculates the difference rate between the current operating condition topology and the switching operating condition topology. The difference rate is the product of the difference weight of the number of nodes and the proportion of the difference of nodes, and the product of the difference weight of the connection relationship and the proportion of the difference of the connection relationship. The node difference percentage is the difference between the number of nodes in the current topology and the number of nodes in the target operating condition, divided by the number of nodes in the target operating condition. For example, if the current topology has 12 nodes and the target operating condition has 8 nodes, the node difference percentage is 4 / 8 = 50%. The connection relationship difference percentage is the quotient of the number of unmatched node connections and the total number of connections in the target operating condition. For example, if the target operating condition has 10 sets of node connections and the current topology only matches 6 sets, the connection difference percentage is 4 / 10 = 40%. If the difference rate is less than the minimum threshold for the power control system to adapt, and the connection relationship between the decision node and the execution node is completely matched, it is determined that the current topology can adapt to the target working condition, and there is no need to start the full reconstruction process; otherwise, the reconstruction process will be restarted.

[0012] In a preferred embodiment, after the decision node generates a topology reconfiguration instruction, it issues a reconfiguration instruction, which includes the binary ID of each control node, the node connection order, and the timing of disconnecting old connections. First, the non-core node connections are disconnected, and then the redundant connections of the core nodes are disconnected. The reconfiguration instruction is synchronously issued to all involved control nodes through electromagnetic induction coupling communication. After receiving the instruction, each node stops sending data, disconnects the current connection, and clears the old connection parameters. After receiving disconnection confirmation signals from all control nodes, the decision node issues a new topology connection command. Each control node establishes a new connection according to the connection order corresponding to the working scenario, forming a self-organizing network topology structure for each working scenario. This includes: the sensing node cluster first establishes communication with the decision node, and then the decision node establishes a connection with the execution node group. During the connection process, the signal transmission priority is automatically matched. After the new topology connection is established, the decision node starts connectivity detection and sends a test signal to each control node to detect the signal transmission status between control nodes.

[0013] In a preferred embodiment, electromagnetic induction coupling is achieved by installing an electromagnetic induction coupling communication module on each control node, the module comprising an electromagnetic signal transmitter, a receiver, and a signal amplification unit.

[0014] The beneficial effects of this invention are: it breaks through the limitations of traditional fixed topology structures, customizes exclusive self-organizing network topologies for different working conditions such as acceleration, turning, and emergency response, realizes resource scheduling, avoids energy waste from full-load operation of all nodes, and improves the pertinence and accuracy of power control under different working conditions by tilting resources towards core control needs, thus solving the problems of poor adaptability and resource redundancy in traditional systems. Within the same type of operating condition, parameter adjustments only update control parameters and do not trigger reconfiguration, ensuring the continuity of power output. When switching between different types of operating conditions, the difference rate is used to determine whether the current topology is suitable for the target operating condition. If the difference rate is lower than the threshold and the core connection is matched, there is no need for complete reconfiguration. Only key nodes are activated to reduce control interruptions and time losses caused by invalid reconfiguration. Attached Figure Description

[0015] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0016] The present invention will now be further described with reference to the accompanying drawings.

[0017] This embodiment provides: an optimization design method for a ship power control system, the method comprising the following steps: S101. Physically deploy the control nodes, which include sensing nodes, decision nodes, and execution nodes, within the system, and complete the input of node IDs and basic information. S102. Based on the ship's operating scenarios, divide its working conditions into scenarios, and set the corresponding self-organizing network topology of control nodes based on each working condition to complete dynamic topology pre-configuration and rule setting. S103. Based on the sensing nodes and execution nodes, complete the collection of working condition data, status feedback data and operation instructions to form a three-dimensional monitoring dataset. Then, through the working condition identification algorithm built into the decision node, analyze it in real time to complete the working condition identification and threshold comparison. Based on the working condition identification and threshold comparison results, complete the topology adaptability judgment. S104. The decision node issues a reconstruction command based on the judgment result, disconnects from the old connection, establishes a new topology connection, and verifies the topology connectivity and stability, thus completing the optimization design of the power control system.

[0018] In S101, the physical deployment of control nodes is determined by the installation location based on the ship's propulsion system layout, including: Sensing nodes are deployed in power equipment and steam pipelines, decision nodes are deployed in the control compartment, and execution nodes are coaxially installed with the system's actuators, such as forward actuators, steering actuators, and braking actuators. After deployment, each control node is assigned a unique binary ID, such as 01 for sensing nodes, 02 for decision nodes, and 03 for execution nodes.

[0019] The operating scenarios are divided according to the ship's operating conditions, including acceleration, turning, constant speed, braking, and emergency conditions. A corresponding self-organizing network topology for control nodes is set for each operating scenario.

[0020] Accelerate the operation; retain all sensing nodes, focus on activating the turbine speed and steam injection quantity sensing nodes, call decision nodes, and establish connections between execution nodes in the order of forward actuator nodes, transmission regulation actuator nodes, and auxiliary stabilization actuator nodes, and establish a self-organizing network topology structure of sensing node cluster, decision nodes, and priority execution node sequence; Steering condition; increase the signal weight of the steering angle and hull tilt sensing nodes, call the decision node, and establish connections between the execution nodes in the order of forward actuator node, transmission adjustment actuator node, and auxiliary stabilization actuator node, and establish a self-organizing network topology of steering-related sensing nodes, decision nodes, and steering-specific execution node groups; Emergency conditions: Skip non-critical sensing nodes and retain only core sensing nodes such as turbine failure, braking pressure, and hull structural stress. Decision nodes are activated, and only emergency braking, emergency power cut-off, and emergency steering actuator nodes are retained as execution nodes. The topology is compressed into core sensing nodes, decision nodes, and emergency execution nodes to ensure the shortest control path. Uniform speed operation and braking operation; referring to the above logic, set low energy consumption topology rules to reduce the number of redundant sensing nodes in operation, and with kinetic energy recovery topology rules, prioritize the activation of execution nodes related to energy feedback closed-loop control. In S103, the decision node receives the collected data from all sensing nodes and the status feedback data from the execution nodes in real time through a preset communication protocol, and at the same time receives the operation instructions from the ship's bridge to form a three-dimensional monitoring dataset. The working condition recognition algorithm built into the decision node extracts key feature parameters from the three-dimensional monitoring dataset and constructs a working condition recognition vector. The key feature parameters include operation command features, perception data features, and execution status features. Operation command features include extracting command type, core parameter amplitude, and parameter change rate. Perception data features include extracting the change trend of core perception parameters and parameter fluctuation amplitude. Execution status features include extracting the action response delay and action completion rate of the execution node. After acquiring key feature parameters, the working condition identification algorithm performs real-time analysis including: Preliminary operating condition classification is performed based on the characteristics of the operating commands. The preliminary operating condition classification corresponds to the divided operating condition scenarios, including acceleration, steering, constant speed, braking, and emergency scenarios. The classification process is specifically based on the data collected by the control node corresponding to the operating condition scenario, the status feedback data, and the operating commands. For example, if the command type is detected as acceleration and the opening degree of the forward actuator is ≥50%, it is initially determined to be an acceleration operating condition. If the command type is detected as steering and the steering wheel rotation angle is ≥30, it is initially determined to be a steering operating condition. Based on historical navigation experience, the basic thresholds for switching operating conditions are preset. For example, the threshold for triggering acceleration is that the forward actuator opening is ≥50% and lasts for 1 second; the threshold for triggering braking is that the brake actuator opening is ≥40% or the braking pressure is ≤3MPa; and the threshold for triggering emergency is that the core sensing parameters exceed the safe range. The threshold parameters are stored in the decision node. The decision node compares the extracted feature parameters with the trigger threshold in real time, and determines the switching of working conditions according to the logic of meeting the threshold conditions and the duration reaching the standard.

[0021] Also includes: For the same type of working condition adjustment; if it does not exceed the basic threshold range of its working condition, it will not trigger topology reconfiguration, but will only update the control parameters. For example, in the acceleration working condition, if the opening of the forward actuator increases from 50% to 70%, but does not exceed the threshold range of the acceleration working condition, it is determined to be a parameter adjustment within the acceleration working condition, which will not trigger topology reconfiguration, but will only update the control parameters. When switching between different operating conditions, if the switch exceeds the basic threshold range of the operating condition but does not exceed the basic threshold range of the emergency operating condition, topology reconstruction will not be triggered and control parameters will be updated. Alternatively, if the switch exceeds the basic threshold range of the operating condition but is outside the basic threshold range of the emergency operating condition, topology adaptability judgment will be triggered and control parameters will be updated.

[0022] In a constant speed condition, if the opening of the forward actuator increases from 30% to 60% and lasts for 1.5s, it meets the trigger threshold for acceleration, and is determined to be a switch between constant speed and acceleration conditions. In a normal condition, if the turbine speed increases to 300r / min and lasts for 500ms, it meets the trigger threshold for emergency conditions, and is determined to be a switch between normal and emergency conditions, triggering topology reconfiguration.

[0023] After the decision node confirms the switch of working conditions, it calculates the difference rate between the current working condition topology and the switching working condition topology. The difference rate is the product of the difference weight of the number of nodes and the proportion of the difference of nodes, and the product of the difference weight of the connection relationship and the proportion of the difference of the connection relationship. The node difference percentage is the difference between the number of nodes in the current topology and the number of nodes in the target operating condition, divided by the number of nodes in the target operating condition. For example, if the current topology has 12 nodes and the target operating condition has 8 nodes, the node difference percentage is 4 / 8 = 50%. The connection relationship difference percentage is the quotient of the number of unmatched node connections and the total number of connections in the target operating condition. For example, if the target operating condition has 10 sets of node connections and the current topology only matches 6 sets, the connection difference percentage is 4 / 10 = 40%. If the difference rate is less than the minimum threshold for the power control system to adapt, and the connection relationship between the decision node and the execution node is completely matched, it is determined that the current topology can adapt to the target working condition, and there is no need to start the full reconstruction process; otherwise, the reconstruction process will be restarted.

[0024] After the decision node generates the topology reconfiguration instruction, it issues the reconfiguration instruction, which includes the binary ID of each control node, the node connection order, and the timing of disconnecting old connections. First, the non-core node connections are disconnected, and then the redundant connections of the core nodes are disconnected. The reconfiguration instruction is synchronously issued to all the relevant control nodes through electromagnetic induction coupling communication. After receiving the instruction, each node stops sending data, disconnects the current connection, and clears the old connection parameters. After receiving disconnection confirmation signals from all control nodes, the decision node issues a new topology connection command. Each control node establishes a new connection according to the connection order corresponding to the working scenario, forming a self-organizing network topology structure for each working scenario. This includes: the sensing node cluster first establishes communication with the decision node, and then the decision node establishes a connection with the execution node group. During the connection process, the signal transmission priority is automatically matched. After the new topology connection is established, the decision node starts connectivity detection and sends a test signal to each control node to detect the signal transmission status between control nodes.

[0025] Electromagnetic induction coupling is achieved by installing an electromagnetic induction coupling communication module on each control node. The module includes an electromagnetic signal transmitter, a receiver, and a signal amplification unit.

Claims

1. A method of optimizing the design of a marine power control system, characterized in that, The method includes the following steps: S101. Physically deploy the control nodes, which include sensing nodes, decision nodes, and execution nodes, within the system, and complete the input of node IDs and basic information. S102. Based on the ship's operating scenarios, divide its working conditions into scenarios, and set the corresponding self-organizing network topology of control nodes based on each working condition to complete dynamic topology pre-configuration and rule setting. S103. Based on the sensing nodes and execution nodes, complete the collection of working condition data, status feedback data and operation instructions to form a three-dimensional monitoring dataset. Then, through the working condition identification algorithm built into the decision node, analyze it in real time to complete the working condition identification and threshold comparison. Based on the working condition identification and threshold comparison results, complete the topology adaptability judgment. S104. The decision node issues a reconstruction command based on the judgment result, disconnects from the old connection, establishes a new topology connection, and verifies the topology connectivity and stability, thus completing the optimization design of the power control system.

2. The method of claim 1, wherein, In step S101, the physical deployment of the control node determines its installation location based on the ship's power system layout, including: Sensing nodes are deployed on power equipment and steam pipelines, decision nodes are deployed in the control compartment, and execution nodes are coaxially installed with the system's actuators. After deployment, each control node is assigned a unique binary ID.

3. The method of claim 2, wherein, The operating scenarios are divided according to the ship's operating conditions, including acceleration, turning, constant speed, braking, and emergency conditions. A corresponding self-organizing network topology for control nodes is set for each operating scenario.

4. The method for optimizing the design of a ship power control system according to claim 1, characterized in that, In S103, the decision node receives the collected data from all sensing nodes and the status feedback data from the execution node in real time through a preset communication protocol, and at the same time receives the operation instructions from the ship's bridge to form a three-dimensional monitoring dataset. The working condition recognition algorithm built into the decision node extracts key feature parameters from the three-dimensional monitoring dataset and constructs a working condition recognition vector. The key feature parameters include operation command features, perception data features, and execution status features. Operation command features include extracting command type, core parameter amplitude, and parameter change rate. Perception data features include extracting the change trend of core perception parameters and parameter fluctuation amplitude. Execution status features include extracting the action response delay and action completion rate of the execution node. After acquiring key feature parameters, the working condition identification algorithm performs real-time analysis including: Preliminary operating condition classification is performed based on the characteristics of the operating instructions. The preliminary operating condition classification corresponds to the divided operating condition scenarios, including acceleration, steering, constant speed, braking, and emergency operating conditions. The classification process specifically involves collecting the data, status feedback data, and operating instructions from the control nodes that correspond to the operating condition scenarios based on the characteristics of the operating instructions. The basic threshold for switching operating conditions is preset based on historical navigation experience, and the threshold parameters are stored in the decision node; The decision node compares the extracted feature parameters with the trigger threshold in real time, and determines the switching of working conditions according to the logic of meeting the threshold conditions and the duration reaching the standard.

5. The method of claim 4, wherein, Also includes: Adjustments to similar operating conditions; If the basic threshold range of its operating conditions is not exceeded, topology reconfiguration will not be triggered and control parameters will not be updated. Switching between different operating conditions; Exceeding If the operating condition is within the basic threshold range and does not exceed the basic threshold range of the emergency operating condition, topology reconstruction will not be triggered and control parameters will not be updated. And / or, if the operating condition exceeds the basic threshold range and is outside the basic threshold range of the emergency operating condition, topology adaptability judgment will be triggered and control parameters will be updated.

6. The method of claim 5, wherein, After the decision node confirms the switch of working conditions, it calculates the difference rate between the current working condition topology and the switching working condition topology. The difference rate is the product of the difference weight of the number of nodes and the proportion of the difference of nodes, and the product of the difference weight of the connection relationship and the proportion of the difference of the connection relationship. The percentage of node differences is the difference between the current number of nodes and the number of nodes in the target operating condition, divided by the number of nodes in the target operating condition. The percentage of connection relationship differences is the quotient of the number of unmatched node connections and the total number of connections in the target operating condition. If the difference rate is less than the minimum threshold for the power control system to adapt, and the connection relationship between the decision node and the execution node is completely matched, it is determined that the current topology can adapt to the target working condition, and there is no need to start the full reconstruction process; otherwise, the reconstruction process will be restarted.

7. The method of claim 1, wherein, After the decision node generates the topology reconfiguration instruction, it issues the reconfiguration instruction, which includes the binary ID of each control node, the node connection order, and the timing of disconnecting old connections. The reconfiguration instruction is synchronously issued to all relevant control nodes through electromagnetic induction coupling communication. After receiving the instruction, each node stops sending data, disconnects the current connection, and clears the old connection parameters. After receiving disconnection confirmation signals from all control nodes, the decision node issues a new topology connection command. Each control node establishes a new connection according to the connection order corresponding to the working scenario, forming a self-organizing network topology structure for each working scenario. After the new topology connection is established, the decision node starts connectivity detection and sends a test signal to each control node to detect the signal transmission between control nodes.

8. The method of claim 7, wherein, Electromagnetic induction coupling is achieved by installing an electromagnetic induction coupling communication module on each control node. The module includes an electromagnetic signal transmitter, a receiver, and a signal amplification unit.

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